Connectionist Approaches to Language Learning (The Springer International Series in Engineering and Computer Science, 154)

ISBN 10: 0792392167 / ISBN 13: 9780792392163

Editore: Springer, 1991

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Dati bibliografici

• Titolo: Connectionist Approaches to Language Learning (The Springer International Series in Engineering and Computer Science, 154)
• Editore: Springer
• Data di pubblicazione: 1991
• Lingua: Inglese
• ISBN 10: 0792392167
• ISBN 13: 9780792392163
• Rilegatura: Rilegato
• Condizione: New

Descrizione articolo

Riassunto

arise automatically as a result of the recursive structure of the task and the continuous nature of the SRN's state space. Elman also introduces a new graphical technique for study­ ing network behavior based on principal components analysis. He shows that sentences with multiple levels of embedding produce state space trajectories with an intriguing self­ similar structure. The development and shape of a recurrent network's state space is the subject of Pollack's paper, the most provocative in this collection. Pollack looks more closely at a connectionist network as a continuous dynamical system. He describes a new type of machine learning phenomenon: induction by phase transition. He then shows that under certain conditions, the state space created by these machines can have a fractal or chaotic structure, with a potentially infinite number of states. This is graphically illustrated using a higher-order recurrent network trained to recognize various regular languages over binary strings. Finally, Pollack suggests that it might be possible to exploit the fractal dynamics of these systems to achieve a generative capacity beyond that of finite-state machines.

Contenuti

Learning Automata from Ordered Examples.- SLUG: A Connectionist Architecture for Inferring the Structure of Finite-State Environments.- Graded State Machines: The Representation of Temporal Contingencies in Simple Recurrent Networks.- Distributed Representations, Simple Recurrent Networks, and Grammatical Structure.- The Induction of Dynamical Recognizers.